Today, interconnected embedded systems called as machine to machine communication systems (M2M) are used in order to establish communication between two machines without need for human. The said M2M systems are systems which are generally powered by battery located on thereof without a power supply that is external and connected to electricity grid and expected to carry on their functions for a long time. For this reason, by calculating power consumptions of M2M systems precisely; operating methods, cycles and times thereof suited for the project should be planned and thus, the most economical energy solution that will fulfil requirement of the system should be selected. In order to ensure that energy consumption remains within economic criteria while expected functions are being carried out, operating statuses which comprise different function sets in M2M systems and thus display different power consumption characteristics are used. The following can be given as example for these operating statuses: deep sleep mode where M2M system can become operative again only by external cut off signal and all systems including processor and RAM are closed, sleep mode where status info of the processor is stored but it is remained in sleep mode and the data stored in the RAM are kept, limited operating mode where processor and peripheral units selected run but communication interfaces are closed, and full operating mode where all components including communication interfaces run actively.
When M2M systems are designed, operating statuses, which are given as example above and can be defined in addition to these, are determined and transitions between these operating statuses are defined in order that M2M systems perform the function to be realized. Power consumption profile of a M2M system that will perform a specific function is displayed by distribution of total power consumption, made by it in order to perform the said function, by time.
In the state of the art, two basic methods are used in order to determine power consumption profiles of M2M systems.
First one of these methods is based on determining amounts of power consumption, which are expected to be consumed by components composing M2M system in different operation statuses, from amounts of nominal consumption, which are involved in technical specification documents for these components. Since it cannot be expected to have common operating status definitions for each component, general system operating statuses are defined and it is tried to match each operating status of components with system operating statuses. The said method increases burden in M2M system design. Even if system power consumption is observed by time, noticing mistakes concerning that matches of component operating status and system operating status anticipated in design could not be realized in practice can be difficult because these observations which comprise additive (cumulative) power information cannot be associated with information of operating statuses. Nominal power consumption values of components included in technical specification documents are theoretical values and may vary by peripheral devices used in practice, application methods and operating time. In order to be able to notice these changes, it is required to measure, observe and evaluate power consumption of each component separately at the stage of prototype production tests. Due to the fact that changes which may occur either in production or because of operating time cannot be noticed, as long as they are not measured, in results of observations made at the stage of prototype; systems designed with this method use up their batteries early because they consume power more differently than expected or huge-sized and costly batteries are used to the extent that they are not needed to meet these changes that cannot be predicted during design.
Another method used for determining power consumption profiles of M2M systems in the state of the art is to set statistical models for devices and operating conditions and make use of simulation applications using these models in design. In the said method: operating conditions which are closest to normal are created for M2M devices; power consumptions of these devices are measured within times when operating status cycles thereof will be repeated a few times; and by constructing time-dependent consumption profile by these measurement results, a statistical model is set by using this profile. In order to set a statistical model without making measurement in an attempt to reduce costs, nominal power consumption values of components provided in technical specification documents are used as well. Deviation of variables included in the statistical model are high and the said deviations cannot be supposed to apply to all operating life of the system designed because it is not possible to provide all conditions, which will be encountered by the M2M device in real life, in case of measurement; know which operating statuses will be operated how often and for how long; and it is tried to predict differences to occur due to production and depreciation. Whereas in a method where no measurement is carried out, addition of greater uncertainties in comparison to statistical model is inevitable. In the event that difference which will occur because of high deviations of variables defining the statistical model is applied to system design, the system offers uneconomic battery solutions or in the event that it is not applied, operating life and capabilities of the system do not take place as designed. In addition, owing to the fact that sensitiveness of the said deviations may be low, it is likely that these deviations fail to satisfy the expectations accordingly even if they are applied to the system design.
The United States patent document no. US2011/0167286 discloses a power management system. The said system comprises: a management console which is in communication with a client computer, a main server, and communication mechanisms within network. The system also comprises power management tools along with one or more computing devices. Computing devices have processor, persistent storage, preferably display screen, operating system, and one or more communication mechanisms such as routers, network keys which are in communication with the power management server. The power management tool is also in communication with one or more power management servers. The power management tool can operate in on-line or off-line statuses. The said power management tool keeps data in chronological order using persistent storage. The power management server is in communication with the power management tool in cases where the power management tool is in on-line status. And, the client computer records principles of power and other databases specific to the client. The power management server and the power management agent are interconnected by means of a secure communication protocol. The power management server records data regarding power use, application use, user activity and system activity which are received from power management tools periodically, in the persistent storage. The said server prepares reports with respect to these data.